Optimization-based approaches are designed to generalize to novel tasks by a few steps of updates. MAML [1]} employs a second-order optimizing strategy with the meta-learning framework by carrying out a small number of update steps. Then Nichol \( et~al. \)  [2]} simplified the implementation of MAML only with the first-order derivation and presented Reptile to move meta-learner parameters towards base-learner parameters. Follow this line, many variants [3]}, [4]}, [5]}, [6]} have made incremental changes for MAML. For example, LEO [3]} applies a low-dimensional latent space to update parameters, which is more practical in low-data scenarios. Since the shared initialization may lead to conflict over tasks, Baik \(et\ al\) . [4]} proposed a task-and-layer-wise attenuation to forget the prior information selectively.
